Be Curious

Be curious, Be foolish | ML engineer exploring the world of AI

[Be Curious @Cuurious_guy]

@dahi_kachorie Chihuahua

[Be Curious @Cuurious_guy]

@Jennnyyyyyy 18KG

[Tech Fusionist @techyoutbe]

MLOps Roadmap (Core topics to focus on)

Programming Fundamentals
- Python
- Go
- Bash

Version Control Systems
- Git
- GitHub

Cloud Computing
- AWS / Azure / GCP
- Cloud-native ML Services

Containerization
- Docker
- Kubernetes

Machine Learning Fundamentals

Data Engineering Fundamentals
- Data Pipelines
- Airflow
- Data Lakes & Warehouses
- Data Ingestion Architecture
- Spark
- Kafka
- Flink

MLOps Principles
MLOps Components
- Version Control
- CI/CD
- Orchestration
- Experiment Tracking & Model Registry
- Data Lineage & Feature Stores
- Model Training & Serving
- Monitoring & Observability

Infrastructure as Code

[Ahmad @TheAhmadOsman]

step-by-step LLM Engineering Projects

each project = one concept learned the hard (i.e. real) way

Tokenization & Embeddings

> build byte-pair encoder + train your own subword vocab
> write a “token visualizer” to map words/chunks to IDs
> one-hot vs learned-embedding: plot cosine distances

Positional Embeddings

> classic sinusoidal vs learned vs RoPE vs ALiBi: demo all four
> animate a toy sequence being “position-encoded” in 3D
> ablate positions—watch attention collapse

Self-Attention & Multihead Attention

> hand-wire dot-product attention for one token
> scale to multi-head, plot per-head weight heatmaps
> mask out future tokens, verify causal property

transformers, QKV, & stacking

> stack the Attention implementations with LayerNorm and residuals → single-block transformer
> generalize: n-block “mini-former” on toy data
> dissect Q, K, V: swap them, break them, see what explodes

Sampling Parameters: temp/top-k/top-p

> code a sampler dashboard — interactively tune temp/k/p and sample outputs
> plot entropy vs output diversity as you sweep params
> nuke temp=0 (argmax): watch repetition

KV Cache (Fast Inference)

> record & reuse KV states; measure speedup vs no-cache
> build a “cache hit/miss” visualizer for token streams
> profile cache memory cost for long vs short sequences

Long-Context Tricks: Infini-Attention / Sliding Window

> implement sliding window attention; measure loss on long docs
> benchmark “memory-efficient” (recompute, flash) variants
> plot perplexity vs context length; find context collapse point

Mixture of Experts (MoE)

> code a 2-expert router layer; route tokens dynamically
> plot expert utilization histograms over dataset
> simulate sparse/dense swaps; measure FLOP savings

Grouped Query Attention

> convert your mini-former to grouped query layout
> measure speed vs vanilla multi-head on large batch
> ablate number of groups, plot latency

Normalization & Activations

> hand-implement LayerNorm, RMSNorm, SwiGLU, GELU
> ablate each—what happens to train/test loss?
> plot activation distributions layerwise

Pretraining Objectives

> train masked LM vs causal LM vs prefix LM on toy text
> plot loss curves; compare which learns “English” faster
> generate samples from each — note quirks

Finetuning vs Instruction Tuning vs RLHF

> fine-tune on a small custom dataset
> instruction-tune by prepending tasks (“Summarize: ...”)
> RLHF: hack a reward model, use PPO for 10 steps, plot reward

Scaling Laws & Model Capacity

> train tiny, small, medium models — plot loss vs size
> benchmark wall-clock time, VRAM, throughput
> extrapolate scaling curve — how “dumb” can you go?

Quantization

> code PTQ & QAT; export to GGUF/AWQ; plot accuracy drop

Inference/Training Stacks:

> port a model from HuggingFace to Deepspeed, vLLM, ExLlama
> profile throughput, VRAM, latency across all three

Synthetic Data

> generate toy data, add noise, dedupe, create eval splits
> visualize model learning curves on real vs synth

each project = one core insight. build. plot. break. repeat.

> don’t get stuck too long in theory
> code, debug, ablate, even meme your graphs lol
> finish each and post what you learned

your future self will thank you later

[Be Curious @Cuurious_guy]

@tomcrawshaw01 Guide

[Be Curious @Cuurious_guy]

@GitaShlokas_ Samay

[ISRO @isro]

SpaDeX Docking Update:

🌟Docking Success

Spacecraft docking successfully completed! A historic moment.

Let’s walk through the SpaDeX docking process:

Manoeuvre from 15m to 3m hold point completed. Docking initiated with precision, leading to successful spacecraft capture. Retraction completed smoothly, followed by rigidisation for stability. Docking successfully completed.

India became the 4th country to achieve successful Space Docking.

Congratulations to the entire team! Congratulations to India!
🛰️🛰️

#SPADEX #ISRO

[Be Curious @Cuurious_guy]

@GithubProjects Bug

[Be Curious @Cuurious_guy]

@mannupaaji DSA 🤝 Dev

[Be Curious @Cuurious_guy]

@curiosityonx Please leave me alone

[Be Curious @Cuurious_guy]

@rauchg Switched to python

[Curiosity @CuriosityonX]

What the night sky on Mars may look like...

[Be Curious @Cuurious_guy]

Hands on Machine Learning Book github repository https://t.co/CYLyu0TIlM

[Be Curious @Cuurious_guy]

📕 A nice article about how diffusion models work ? https://t.co/6g3PrNtWQI

[Be Curious @Cuurious_guy]

LLMs Course https://t.co/7IiMFEOfE6

[Be Curious @Cuurious_guy]

Github Repository for project based learning https://t.co/plIEal6IxM

[Be Curious @Cuurious_guy]

Neural Network From Scratch https://t.co/QDwwi93hi2

[Be Curious @Cuurious_guy]

Exceptional results require exceptional efforts #Motivation

[Be Curious @Cuurious_guy]

“100 NLP interview questions” https://t.co/qk29GzoDan

[Be Curious @Cuurious_guy]

“End-to-End Machine Learning Project: Churn Prediction” https://t.co/K1ay2ssUNS